In projection radiography the x-ray scatter occurring in the patient leads to deterioration in the contrast and an increase in the noise. For quantitative applications, such as dual-energy projection imaging, e.g. in accordance with publication [Wa00], or computer tomography (CT), x-ray scatter also leads to quantitative distortions or artifacts which can adversely affect diagnosis. This problem assumes topical interest for CT with flat panel detectors (CBCT=Conebeam CT). Whereas with conventional CT systems with just a few detector rows an effective suppression of x-ray scatter can still be achieved with collimators, with CT with flat panel detectors this is no longer the case and other solutions must be found.
In projection radiography anti-scatter grids are frequently used directly above the detector input surface in order to reduce x-ray scatter. The benefit of anti-scatter grids for CBCT imaging is currently still a controversial subject in discussions but its use is to be recommended at least for a high proportion of x-ray scatter [SMB04]. As a rule however the reduction of x-ray scatter through anti-scatter grids is not sufficient so that additional computational x-ray scatter-correction processes are necessary.
With dual-energy imaging in the thorax area the patient is usually positioned very close to the detector, i.e. operation is with a very small air gap, the result of which is that, despite anti-scatter grids, the x-ray scatter intensity can overwhelm even the primary intensity, above all in the regions of the image with strong attenuation and with higher photon energies corresponding to x-ray tube voltages of >100 kV.
For around 20 years, especially since digital techniques began to spread in radiography, proposals for computational correction of x-ray scatter have been published in technical literature. Since more precise computing methods, such as Monte Carlo models for example, involve far too much effort for real-time-processing, right from the outset and to date so-called convolution computing models have been discussed. In this connection the reader is referred to the publications [LoK87], [SeB89] and [ASO99].
With the convolution cores used in literature a degree of arbitrariness obtains. Thus for example Love and Kruger in [LoK87] have investigated parametric mathematical convolution cores, i.e. rectangular, triangular, Gaussian and exponential cores. In these cases however the authors use homogeneous scatter bodies. Given these simple requirements, the differences for corresponding adaptation of the cores were not very large.
Other authors [BaF00] decide on one core type in advance, a Gaussian or exponential core is preferably used, which appears suitable. Yet other authors [TTK02] have proposed cores dependent on the tissue thickness, e.g. the thickness of the breast in mammography.
Approaches such as those stated above are primarily suitable for conditions in which largely homogeneous material is present, i.e. with relatively small differences in the thickness and the atomic composition. Mammography is a typical example of this.
With strongly inhomogeneous scatter objects, such as with bone structures and soft tissue regions—e.g. cranium, thorax, abdomen, pelvis—purely mathematical convolution cores are less suitable for describing x-ray scatter propagation and deliver unsatisfactory results.